Discover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Discover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Over 700 devices are categorized as “artificial intelligence and machine learning enabled medical devices” on the FDA website. This may create the perception that all AI in healthcare is the same.
Grouping AI technology together like this doesn’t help people implementing AI differentiate between the different types of AI in healthcare – what they do, how they work and, ultimately, how they can impact patient care and provider experience.
This blog post is a guide on how AI is used in healthcare today, the benefits of AI-powered care for various subspecialities and what the future of AI in healthcare could look like.
What is AI?
At the most basic level, artificial intelligence is the intelligence demonstrated by machines, which learn and perform tasks typically completed by humans.
The terms “healthcare AI” and “clinical AI” might seem interchangeable, but there’s a key distinction. Imagine healthcare as a large system with many moving parts. Healthcare AI encompasses all the AI tools used across this system, from administrative tasks like appointment scheduling to operational functions like supply chain management.
Clinical AI, on the other hand, focuses on a specific part of the system: patient care. It leverages various AI techniques like deep learning and natural language processing to directly improve patient outcomes. This includes aiding clinicians in making faster decisions and streamlined paths to treatment.
Common Types of AI In Healthcare
It’s important to acknowledge there are conflicting definitions in healthcare AI – and people use these terms differently – but these can be considered a starting point for awareness.
Machine Learningin Healthcare One of the most common and broad applications of AI, machine learning (ML) uses pattern-based identification that allows a machine to learn and, overtime, apply that experience to similar scenarios. Examples of machine learning in healthcare: Chatbots for billing and scheduling or filtering and organizing data within a medical device, such as an MRI or CT scanner.
Computer Vision (CV) in Healthcare Another form of machine learning, it is the process by which a computer gains information and understanding from images and videos. In some advanced forms of CV, there are deep learning capabilities that can recognize, interpret and categorize images. Healthcare CV example: Medical imaging analysis and facial recognition patient identification software.
Natural Language Processing (NLP) in Healthcare Another form of deep learning, it allows computers to pull data from human language and make decisions based on that information. Examples of NLP in healthcare: Can identify and alert clinicians to patient follow-up opportunities based on clinical notes.
Generative AI in Healthcare This type of machine learning trains various algorithms to create content – from code to text, images and video. The most well-known example is Open AI’s ChatGPT, which has been shown to write summaries or answer questions with lifelike accuracy. Generative AIin healthcare examples: Tools are still in development, but possible use cases could be correspondence and documentation.
The Benefits of AI in Healthcare
Artificial intelligence (AI) for healthcare has the potential to revolutionize healthcare by reducing costs, improving access to care and increasing quality. The key is implementing the right AI deployed as an enterprise-wide system.
There are many benefits of AI in healthcare, but one of the most important benefits is that it allows healthcare providers to overcome data and system fragmentation, analyze vast amounts of data, identify trends and make faster decisions – ultimately leading to better patient outcomes.
Finding the Right AI for Healthcare
Simply deploying any AI healthcare solution isn’t enough. Point solutions, which address specific tasks in isolation, have limitations. They can’t be easily scaled across different departments or use cases, requiring significant IT resources to maintain.
The real power of AI in healthcare lies in its ability to connect the dots. A “connected system of intelligence” brings together various medical devices and platforms, acting as a central nervous system for hospitals. This approach offers immediate benefits by tackling both clinical and financial challenges. It requires an AI-based platform (aiOS™) that can bridge the gap between different departments and data sources.
AI and Healthcare: Doing More With Fewer Resources
With an AI platform to bring together disparate devices and solutions, clinicians and health systems can see immediate value.
AI in Clinical Support
Automating administrative tasks: AI can automate repetitive tasks, including data entry, scheduling and charting allowing more time for direct patient care.
Reducing staff burnout: AI reduces the amount of time spent on non-value-added tasks to improve overall efficiency while reducing cognitive load.
Improving diagnostic efficiency: AI supports clinicians in making accurate and timely decisions that can lead to more confident decision-making.
Health System Optimization
Resource allocation: AI can appropriately triage resources to address emergent conditions at every patient touchpoint.
Reducing operational waste: Variability is the enemy of efficiency. AI enables better quality care that provides downstream efficiency improvements. For example, reducing patient length of stay and lowering readmissions.
ROI and the Quadruple Aim
When implemented as an enterprise-wide platform, AI can deliver a ripple effect return on investment (ROI). AI-assisted physicians and health systems can find new revenue streams through clinically-appropriate interventions, including incidental findings and patient retention. But the advantages go beyond the bottom line.
AI can directly contribute to achieving the quadruple aim, a framework that prioritizes patient experience, population health, cost reduction and provider well-being. By enabling personalized care, increasing disease awareness and streamlining processes, AI in healthcare creates a win-win situation for both patients and providers..
AI in Healthcare Examples
Physicians in every subspeciality are searching for efficiencies at every patient and collaboration touchpoint. That’s where AI platforms can help. Here are ways some clinicians are using AI-powered healthcare today.
Radiology
Radiologists leverage AI in healthcare to automate tasks and improve workflow efficiency. These radiology AI tools act as intelligent assistants, sifting through massive amounts of scans, like X-rays, CTs and MRIs, flagging potentially critical abnormalities.
Triage and Notification Support
Triage and notification support allows radiologists to prioritize urgent cases, streamlining workflows and helping to ensure faster time-to-treatment. Many AI healthcare companies have solutions that integrate directly into the radiologist workstation, allowing them to easily send notifications and alerts to other specialists.
This translates to reduced pressure on radiologists, quicker turnaround time on reports and better collaboration amongst departments – and hub and spoke facilities — which can lead to improved patient outcomes and a shorter length of stay.
AI is revolutionizing cardiovascular care by offering several benefits to both clinicians and patients. Because cardiovascular AI can analyze vast amounts of data – including imaging, medical records and radiology reports – it can identify potentially life-threatening conditions, like coronary artery disease, earlier.
Care Coordination and Patient Follow-Up Management
Several AI healthcare companies offer care coordination and patient management solutions to improve cross-specialty communication and streamline care for patients with both acute and subacute findings. These tools provide real-time updates on critical cases, allowing cardiovascular specialists to assess patient risk factors and view medical images directly on their preferred devices.
With a platform that connects to electronic health records, AI-powered patient management can identify patients who need further follow-up care, track their progress and even automatically reach out to them or their primary care physician to ensure they receive the care they need.
While still an emerging field of AI in healthcare, results have been promising:
A seven hour decrease in time from imaging to thrombectomy association with a three-day reduction in ICU length of stay
40% increase in Pulmonary Embolism Response Team (PERT) consultations
AI-powered healthcare is helping clinicians find and treat neurological conditions more effectively. With the ability to analyze all relevant imaging data based on anatomy present, AI highlights potential abnormalities, allowing care teams from different specialties to collaborate on creating the best treatment plans quickly.
Connecting Teams to Enable Faster Care
For example, AI healthcare company Aidoc has a full brain solution that can identify different types of strokes, including large vessel occlusions (LVOs) and medium vessel occlusions (MeVOs). Using different types of AI, including image-based identification and natural language processing, the AI technology can identify and orchestrate care within existing workflows.
Additionally, the company’s patient management solution uses text-based AI to surface only confirmed positive cases from radiology reports and notify specialists or nurse navigators within the specialty clinic. Here’s an example of what an Aidoc neurology workflow looks like:
Crowded emergency departments (ED) can make it difficult to deliver the best care as quickly as possible. However, AI applications are being used to improve communication between staff, minimize risk and improve workflows, ultimately leading to better patient care.
A key benefit of AI in healthcare is its ability to streamline patient management. If AI helps triage and prioritize suspected cases for the radiologist, they can more quickly communicate those findings to the ED clinicians, reducing length of stay and getting patients the care they need promptly.
The improvement of the patient experience as they are moved through the labyrinth of the hospital is an added benefit of AI in healthcare, ultimately serving the goal of all medical practitioners: improving patient outcomes and how they experience healthcare.
Here is an example with/without AI workflow to show the impact of streamlined patient management in the ED:
AI Healthcare Technology Integrations
The ability for different systems and devices to exchange and use information with one another – also known as interoperability – is essential to AI’s success in healthcare. Without it, information is fragmented, data is siloed and the scalability of AI solutions will be limited.
When preparing to implement AI in healthcare, it is important to understand how AI healthcare technology companies connect to multiple service lines and systems with a seamless user-experience and low maintenance burden on IT teams. While some AI is built into medical imaging devices, like CT, MRI, ECG, etc., if they aren’t being powered by an AI platform, the functionality of that AI will be limited to only those devices. Without a platform to act as the single, vendor agnostic and unified operating system, it is difficult to realize the full benefits of AI in healthcare.
Want to learn about the different AI integration options available to you?Download our free white paper to learn more:
Must-Have AI Healthcare Technology Integrations
Picture Archiving and Communication System (PACS)
Vendor Neutral Archive (VNA)
Radiology Worklist
Electronic Health Records (EHR)
Scheduling System
Mobile Communication Systems
Finding the Right Healthcare AI Vendor
Remember – when preparing for AI adoption, you aren’t selecting an algorithm but choosing a partner. Not all vendors are created equal, and one of the biggest differences is in their ability to support enterprise-wide AI technology integrations. This checklist can help you determine if a vendor can meet your integration needs.
Implementing AI in Healthcare
Like healthcare itself, paths to clinical AI implementation haven’t been one-size-fits-all. Three approaches have gained traction, each with its own unique advantages and challenges.
Point solutions offer rapid deployment and focus on specific tasks but can create data silos and limit scalability.
Marketplaces offer a hub for discovering and choosing different AI solutions but require expertise to navigate and may involve varying quality and system compatibility.
Platforms provide a centralized infrastructure for scalable AI implementation but require upfront investment.
How point solutions, marketplaces and platforms mitigate the risks associated with large-scale technology shifts is ultimately what differentiates the integration approaches from one another. It is important to consider the data, infrastructure and human challenges your health system will need to address prior to selecting a vendor.
What is a Common Healthcare AI Implementation Process?
Implementing AI in healthcare can seem complex, but focusing on three key areas can make it smoother:
First, identify your problem statement and use cases, such as prioritizing urgent cases in radiology or coordinating care teams in emergency departments.
Second, choose the right vendor. Look for experience, regulatory compliance and a commitment to working with you to integrate AI seamlessly into your existing workflows.
Finally, the integration process itself can be straightforward with the right partner. Well-designed AI solutions minimize disruption and can even be deployed remotely.
Every vendor will have a slightly different approach to AI implementation, but the most crucial first step is to view AI implementation as more than a technological undertaking. It doesn’t start with algorithms, it starts with strategy. A technology plus people plus process-focused strategy will address the fragmented and piecemeal adoption of AI that has dominated the first wave of AI in healthcare implementation. The largest impact on your short-term needs and long-term infrastructure usability is the AI integration method you choose. Here are some resources to help you get started with AI strategy and implementation.
The Role of AI in the Future of Healthcare
The landscape of AI technology is evolving at an unprecedented pace, and the future remains largely unpredictable. Yet, each year a few trends emerge.
Emergence of Multimodal
The future of AI in healthcare is all about multimodal analysis and robust platforms. Instead of just analyzing images, AI will increasingly combine them with a patient’s clinical history from their electronic medical records (EHR). This will free up clinicians from tedious information gathering and allow them to focus on more complex tasks.
The Platform Imperative
To achieve multimodal analysis, AI platforms will need to bridge the gap between imaging and clinical data. For example, an incidental finding on an image may seem insignificant without a patient’s history of smoking or high blood pressure. By combining this information, AI can provide a more complete picture and improve the quality of radiology reports.
Increased Governance
As AI becomes more powerful, governance will become even more critical. Hospitals and healthcare institutions are forming AI Governance Committees to define how they will use and integrate these new technologies. This is happening alongside the development of regulatory frameworks by organizations like the Office of the National Coordinator for Health Information Technology (ONC). These regulations will ensure that AI is used safely and effectively in the healthcare industry.
Want to learn more about AI Governance? Watch our webinar on demand for free:
AI in Healthcare: Getting Started
AI is changing healthcare in numerous ways but implementation is not one-size-fits-all or a linear path. Aidoc is committed to helping hospitals and health systems navigate the AI hype cycle and build scalable and sustainable AI strategies that meet short-term needs and long-term opportunities. These resources can help you get started:
